Springer Handbook of Computational Intelligence by Janusz KacprzykSpringer Handbook of Computational Intelligence by Janusz Kacprzyk

Springer Handbook of Computational Intelligence

byJanusz KacprzykEditorWitold Pedrycz

Hardcover | May 18, 2015

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The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts:  foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.
Janusz Kacprzyk is editor in chief of the Springer series Studies in "Computational Intelligence", "Studies in Fuzziness and Soft computing", "Advances in Intelligent and Soft Computing" and "Intelligent Systems Reference Library". He is the recent past president of the International Fuzzy Systems Association (IFSA) and recipient of th...
Title:Springer Handbook of Computational IntelligenceFormat:HardcoverDimensions:1634 pagesPublished:May 18, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3662435047

ISBN - 13:9783662435045

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Table of Contents

Many-Valued and Fuzzy Logics.- Possibility Theory and its Applications: Where Do we Stand?.- Aggregation Functions on [0; 1].- Monotone Measures-Based Integrals.- The Origin of Fuzzy Extensions.- From Type-2 Fuzzy Sets to Atanassov's Intuitionistic Fuzzy Sets.- F-Transform.- Fuzzy Linear Programming and Duality.- Basic Solutions of Fuzzy Coalitional Games.- Basics of Fuzzy Sets.- Fuzzy Relations: Past, Present and Future.- Fuzzy Implications.- Fuzzy Rule Based Systems.- Interpretability of Fuzzy Systems.- Fuzzy Clustering.- An Algebraic Model of Reasoning to Support Zadeh's CwW.- Fuzzy Control.- Interval Type-2 Fuzzy PID Controllers.- Soft Computing in Database and Information Management.- Application of Fuzzy Techniques to Autonomous Robots.- Foundations of Rough Sets.- Rough Set Methodology for Decision Aiding.- Rule Induction from Rough Approximations.- Probabilistic Rough Sets.- Generalized Rough Sets.- Fuzzy-Rough Hybridization.- Artificial Neural Network Models .- Deep and Modular Neural Networks.- Machine Learning.- Theoretical Methods in Machine Learning.- Probabilistic Modeling in Machine Learning.- Kernel Methods.- Neurodynamics .- Computational Neuroscience - Biophysical Modeling of Neural Systems .- Computational Models of Cognitive and Motor Control.- Cognitive Architectures and Agents.- Embodied Intelligence.- Neuromorphic Engineering.- Neuroengineering -- Sensorimotor-Computer Interfaces.- Evolving Connectionist Systems .- Machine Learning Applications.- Genetic Algorithms.- Genetic Programming.- Evolution Strategies.- Distribution Algorithms.- Parallel Evolutionary Algorithms.- Learning Classifier Systems.- Indicator-Based Selection.- Multiobjective Evolutionary Algorithms.- Parallel Multiobjective Evolutionary Algorithms.- Many-objective Problems: Challenges and Methods.- Memetic and Hybrid Evolutionary Algorithms.- Design of Representations and Search Operators.- Stochastic Local Search Algorithms.- Parallel Evolutionary Combinatorial Optimization.- How to Create Generalizable Results.- Computational Intelligence in Industrial Applications.- Solving Phase Equilibrium Problems.- Optimization of Machining Problems.- Physics-Based Surrogate Modelling in Evolutionary Optimization for Aerodynamic Design.- Evolutionary Combinatorial Optimization for Knowledge Discovery in Bioinformatics .- Integration of Metaheuristics and Constraint Programming.- Assessing the Effects of Recombination with the Graph Coloring Problem.- Metaheuristic Algorithms and Tree Decomposition.- Evolutionary Computation and Constraint Satisfaction.- Swarm Intelligence in Optimization and Robotics.- Preference-Based Multiobjective Particle Swarm Optimization.- Ant Colony Optimization for the Minimum-Weight Rooted Arborescence Problem.- Intelligent Swarm of Markovian Agents.- Honey Bee Social Foraging Algorithm.- Fundamental Collective Behaviors in Swarm Robotics.- Collective Manipulation and Construction.- Reconfigurable Robots.- Probabilistic Modeling of Swarming Systems.- Robust Evolving Cloud-Based Controller.- Evolving Embedded Fuzzy Controllers.- Multiobjective Genetic Fuzzy Systems.- Bio-Inspired Optimization.- Pattern Recognition with Modular Neural Networks.- Optimization of Interval Fuzzy Controllers for Autonomous Mobile Robots.- Implementation of Bio-Inspired Optimization Methods on Graphic Processing Units.- Acknowledgements.- About the Authors.- Subject Index.

Editorial Reviews

"This is not just a hand book but an encyclopedia/reference tool for computer scientists, engineers (bio, neuro, electric), and physicists dealing with complex mathematical problems. It describes the basic principles, theories, and historical development of Logic, computational sciences, EE, and neurophysiological background for modeling." (Joseph J. Grenier, Amazon.com, April, 2016)